| A support vector machine–firefly algorithm-based model for global solar radiation prediction L Olatomiwa, S Mekhilef, S Shamshirband, K Mohammadi, D Petković, ... Solar Energy 115, 632-644, 2015 | 382 | 2015 |
| A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation K Mohammadi, S Shamshirband, CW Tong, M Arif, D Petković, S Ch Energy Conversion and Management 92, 162-171, 2015 | 274 | 2015 |
| A hybrid SVM-PSO model for forecasting monthly streamflow C Sudheer, R Maheswaran, BK Panigrahi, S Mathur Neural Computing and Applications 24, 1381-1389, 2014 | 190 | 2014 |
| Soft computing approaches for forecasting reference evapotranspiration M Gocić, S Motamedi, S Shamshirband, D Petković, S Ch, R Hashim, ... Computers and Electronics in Agriculture 113, 164-173, 2015 | 182 | 2015 |
| Streamflow forecasting by SVM with quantum behaved particle swarm optimization S Ch, N Anand, BK Panigrahi, S Mathur Neurocomputing 101, 18-23, 2013 | 169 | 2013 |
| An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India C Suryanarayana, C Sudheer, V Mahammood, BK Panigrahi Neurocomputing 145, 324-335, 2014 | 164 | 2014 |
| A support vector machine-firefly algorithm based forecasting model to determine malaria transmission S Ch, SK Sohani, D Kumar, A Malik, BR Chahar, AK Nema, BK Panigrahi, ... Neurocomputing 129, 279-288, 2014 | 139 | 2014 |
| Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm ET Al-Shammari, A Keivani, S Shamshirband, A Mostafaeipour, L Yee, ... Energy 95, 266-273, 2016 | 120 | 2016 |
| Application of artificial neural networks and particle swarm optimization for the management of groundwater resources S Gaur, S Ch, D Graillot, BR Chahar, DN Kumar Water resources management 27 (3), 927-941, 2013 | 95 | 2013 |
| Predicting the wind power density based upon extreme learning machine K Mohammadi, S Shamshirband, L Yee, D Petković, M Zamani, S Ch Energy 86, 232-239, 2015 | 94 | 2015 |
| Surface roughness prediction by extreme learning machine constructed with abrasive water jet Ž Ćojbašić, D Petković, S Shamshirband, CW Tong, S Ch, P Janković, ... Precision Engineering 43, 86-92, 2016 | 92 | 2016 |
| RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution S Shamshirband, K Mohammadi, CW Tong, D Petković, E Porcu, ... Climate dynamics 46, 1893-1907, 2016 | 83 | 2016 |
| Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models JABN Maheswaran Rathinasamy, Rakesh Khosa, Jan Adamowski, Sudheer ch ... Water resources research, 2014 | 83 | 2014 |
| Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, Germany B Yadav, S Ch, S Mathur, J Adamowski Measurement 92, 433-445, 2016 | 79 | 2016 |
| Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction B Yadav, S Ch, S Mathur, J Adamowski Journal of water and land development 32 (1), 103, 2017 | 76 | 2017 |
| Long‐term precipitation analysis and estimation of precipitation concentration index using three support vector machine methods M Gocic, S Shamshirband, Z Razak, D Petković, S Ch, S Trajkovic Advances in Meteorology 2016 (1), 7912357, 2016 | 69 | 2016 |
| Extreme learning machine approach for sensorless wind speed estimation V Nikolić, S Motamedi, S Shamshirband, D Petković, S Ch, M Arif Mechatronics 34, 78-83, 2016 | 66 | 2016 |
| Groundwater level forecasting using soft computing techniques N Natarajan, C Sudheer Neural Computing and Applications 32, 7691-7708, 2020 | 65 | 2020 |
| Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM)-particle swarm optimization approach B Yadav, S Ch, S Mathur, J Adamowski Journal of hydrology 543, 373-385, 2016 | 64 | 2016 |
| Particle swarm optimization trained neural network for aquifer parameter estimation S Ch, S Mathur KSCE Journal of Civil Engineering 16, 298-307, 2012 | 62 | 2012 |
